Role description
Who we are: -
At
CitiusTech , we constantly strive to solve the industry's greatest challenges with technology, creativity, and agility. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation, and industry-wide convergence for over 140 organizations through next-generation technologies, solutions, and products. We aim to accelerate the transition to a human-first, sustainable, and digital healthcare ecosystem with the world's leading Healthcare and life sciences organizations and our partners.
Here is an opportunity for you to make a difference and collaborate with global leaders to shape the future of healthcare and positively impact human lives.
Our vision: -
To inspire new possibilities for the health ecosystem with technology and human ingenuity.
Role: Agentic AI Engineer
Location: Rochester, MN (Remote)
Responsibilities:
Design, build, and productionize ML models for personalization, forecasting, anomaly detection, and NLP on GCP (Vertex AI, BigQuery ML, Dataflow).
Develop scalable data and feature pipelines; implement feature stores (Feast) and streaming ingestion (Pub/Sub, Kafka) for real-time inference.
Implement agentic AI patterns: multi-agent orchestration, tool-use, retrieval-augmented generation (RAG), and function calling with guardrails (policy/PII filters).
Optimize online inference for latency and cost using Vertex AI Endpoints, GPU/TPU where applicable, and autoscaling on GKE.
Establish experiment frameworks (A/B, interleaving, bandits) and offline evaluation (precision/recall, ROC-AUC, NDCG, MAP).
Integrate vector search (Vertex Matching Engine / FAISS / Elasticsearch) for semantic retrieval and recommendations.
Ensure privacy, security, and compliance (GDPR/CCPA); apply differential privacy where needed and follow model governance practices. Document designs, review code, and collaborate with product, data engineering, and platform teams.
Desired Technical Skills Languages:
Python (primary), Java/Scala (nice to have).
Frameworks:
TensorFlow, PyTorch, scikit-learn; JAX optional.
Pipelines & Features:
Apache Beam/Dataflow, Airflow/Cloud Composer, Feast.
RAG & Agents: LangChain/LlamaIndex, function calling, toolformer patterns, vector DBs (FAISS, Chroma, Elasticsearch).
Data & Streaming:
BigQuery, Spark, Kafka, Pub/Sub.
Serving:
Vertex AI Endpoints, KFServing, Triton; Docker, Kubernetes (GKE).
Observability:
MLflow, Vertex Experiments/Model Registry, Prometheus, Grafana.
Cloud:
GCP (Vertex AI, BigQuery, GCS) primary; familiarity with AWS (SageMaker) and Azure ML.
Preferred Experience & Capabilities Delivered ML systems at scale (batch + real-time) with measurable business impact.
Hands-on with vector search, RAG, and agentic workflows using tools/actions and governance (safety filters, jailbreak protection).
Expertise in feature engineering, data quality, and drift detection (data/model).
Strong understanding of IR/ranking metrics and online experimentation.
Ability to mentor, perform code reviews, and contribute to architectural decisions. Excellent communication with cross-functional stakeholders.
Note:
Primary development environment is GCP (Google Cloud Platform). Flexibility to work with AWS and Azure is desired for portability and interoperability.
Skills
AI